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<p>A layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function:  
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Namespaces</h2></td></tr>
<tr class="memitem:namespacecaffe_1_1SolverAction"><td class="memItemLeft" align="right" valign="top"> &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacecaffe_1_1SolverAction.html">SolverAction</a></td></tr>
<tr class="memdesc:namespacecaffe_1_1SolverAction"><td class="mdescLeft">&#160;</td><td class="mdescRight">Enumeration of actions that a client of the <a class="el" href="classcaffe_1_1Solver.html" title="An interface for classes that perform optimization on Nets. ">Solver</a> may request by implementing the <a class="el" href="classcaffe_1_1Solver.html" title="An interface for classes that perform optimization on Nets. ">Solver</a>'s action request function, which a client may optionally provide in order to request early termination or saving a snapshot without exiting. In the executable caffe, this mechanism is used to allow the snapshot to be saved when stopping execution with a SIGINT (Ctrl-C). <br /></td></tr>
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Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1AbsValLayer.html">AbsValLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes <img class="formulaInl" alt="$ y = |x| $" src="form_9.png"/>.  <a href="classcaffe_1_1AbsValLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1AccuracyLayer.html">AccuracyLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the classification accuracy for a one-of-many classification task.  <a href="classcaffe_1_1AccuracyLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1AdaDeltaSolver.html">AdaDeltaSolver</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1AdaGradSolver.html">AdaGradSolver</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1AdamSolver.html">AdamSolver</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a class="el" href="classcaffe_1_1AdamSolver.html" title="AdamSolver, an algorithm for first-order gradient-based optimization of stochastic objective function...">AdamSolver</a>, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Described in [1].  <a href="classcaffe_1_1AdamSolver.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ArgMaxLayer.html">ArgMaxLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the index of the <img class="formulaInl" alt="$ K $" src="form_25.png"/> max values for each datum across all dimensions <img class="formulaInl" alt="$ (C \times H \times W) $" src="form_29.png"/>.  <a href="classcaffe_1_1ArgMaxLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BaseConvolutionLayer.html">BaseConvolutionLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Abstract base class that factors out the BLAS code common to <a class="el" href="classcaffe_1_1ConvolutionLayer.html" title="Convolves the input image with a bank of learned filters, and (optionally) adds biases. ">ConvolutionLayer</a> and <a class="el" href="classcaffe_1_1DeconvolutionLayer.html" title="Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and ...">DeconvolutionLayer</a>.  <a href="classcaffe_1_1BaseConvolutionLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BaseDataLayer.html">BaseDataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides base for data layers that feed blobs to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a>.  <a href="classcaffe_1_1BaseDataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BasePrefetchingDataLayer.html">BasePrefetchingDataLayer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Batch.html">Batch</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BatchNormLayer.html">BatchNormLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Normalizes the input to have 0-mean and/or unit (1) variance across the batch.  <a href="classcaffe_1_1BatchNormLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BatchReindexLayer.html">BatchReindexLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Index into the input blob along its first axis.  <a href="classcaffe_1_1BatchReindexLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BiasLayer.html">BiasLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes a sum of two input Blobs, with the shape of the latter <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> "broadcast" to match the shape of the former. Equivalent to tiling the latter <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>, then computing the elementwise sum.  <a href="classcaffe_1_1BiasLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BilinearFiller.html">BilinearFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with coefficients for bilinear interpolation.  <a href="classcaffe_1_1BilinearFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Blob.html">Blob</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">A wrapper around <a class="el" href="classcaffe_1_1SyncedMemory.html" title="Manages memory allocation and synchronization between the host (CPU) and device (GPU). ">SyncedMemory</a> holders serving as the basic computational unit through which <a class="el" href="classcaffe_1_1Layer.html" title="An interface for the units of computation which can be composed into a Net. ">Layer</a>s, <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a>s, and <a class="el" href="classcaffe_1_1Solver.html" title="An interface for classes that perform optimization on Nets. ">Solver</a>s interact.  <a href="classcaffe_1_1Blob.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BlockingQueue.html">BlockingQueue</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1BNLLLayer.html">BNLLLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes <img class="formulaInl" alt="$ y = x + \log(1 + \exp(-x)) $" src="form_41.png"/> if <img class="formulaInl" alt="$ x &gt; 0 $" src="form_42.png"/>; <img class="formulaInl" alt="$ y = \log(1 + \exp(x)) $" src="form_43.png"/> otherwise.  <a href="classcaffe_1_1BNLLLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Caffe.html">Caffe</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ConcatLayer.html">ConcatLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Takes at least two <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>s and concatenates them along either the num or channel dimension, outputting the result.  <a href="classcaffe_1_1ConcatLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ConstantFiller.html">ConstantFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with constant values <img class="formulaInl" alt="$ x = 0 $" src="form_0.png"/>.  <a href="classcaffe_1_1ConstantFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ContrastiveLossLayer.html">ContrastiveLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the contrastive loss <img class="formulaInl" alt="$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2 $" src="form_51.png"/> where <img class="formulaInl" alt="$ d = \left| \left| a_n - b_n \right| \right|_2 $" src="form_52.png"/>. This can be used to train siamese networks.  <a href="classcaffe_1_1ContrastiveLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ConvolutionLayer.html">ConvolutionLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Convolves the input image with a bank of learned filters, and (optionally) adds biases.  <a href="classcaffe_1_1ConvolutionLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1CPUTimer.html">CPUTimer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1CropLayer.html">CropLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Takes a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> and crop it, to the shape specified by the second input <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>, across all dimensions after the specified axis.  <a href="classcaffe_1_1CropLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1DataLayer.html">DataLayer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1DataTransformer.html">DataTransformer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Applies common transformations to the input data, such as scaling, mirroring, substracting the image mean...  <a href="classcaffe_1_1DataTransformer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1DeconvolutionLayer.html">DeconvolutionLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as <a class="el" href="classcaffe_1_1ConvolutionLayer.html" title="Convolves the input image with a bank of learned filters, and (optionally) adds biases. ">ConvolutionLayer</a>.  <a href="classcaffe_1_1DeconvolutionLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1DropoutLayer.html">DropoutLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">During training only, sets a random portion of <img class="formulaInl" alt="$x$" src="form_64.png"/> to 0, adjusting the rest of the vector magnitude accordingly.  <a href="classcaffe_1_1DropoutLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1DummyDataLayer.html">DummyDataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> generated by a <a class="el" href="classcaffe_1_1Filler.html" title="Fills a Blob with constant or randomly-generated data. ">Filler</a>.  <a href="classcaffe_1_1DummyDataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1EltwiseLayer.html">EltwiseLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute elementwise operations, such as product and sum, along multiple input Blobs.  <a href="classcaffe_1_1EltwiseLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ELULayer.html">ELULayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Exponential Linear Unit non-linearity <img class="formulaInl" alt="$ y = \left\{ \begin{array}{lr} x &amp; \mathrm{if} \; x &gt; 0 \\ \alpha (\exp(x)-1) &amp; \mathrm{if} \; x \le 0 \end{array} \right. $" src="form_71.png"/>.  <a href="classcaffe_1_1ELULayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1EmbedLayer.html">EmbedLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">A layer for learning "embeddings" of one-hot vector input. Equivalent to an <a class="el" href="classcaffe_1_1InnerProductLayer.html" title="Also known as a &quot;fully-connected&quot; layer, computes an inner product with a set of learned weights...">InnerProductLayer</a> with one-hot vectors as input, but for efficiency the input is the "hot" index of each column itself.  <a href="classcaffe_1_1EmbedLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1EuclideanLossLayer.html">EuclideanLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the Euclidean (L2) loss <img class="formulaInl" alt="$ E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n \right| \right|_2^2 $" src="form_74.png"/> for real-valued regression tasks.  <a href="classcaffe_1_1EuclideanLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ExpLayer.html">ExpLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes <img class="formulaInl" alt="$ y = \gamma ^ {\alpha x + \beta} $" src="form_82.png"/>, as specified by the scale <img class="formulaInl" alt="$ \alpha $" src="form_72.png"/>, shift <img class="formulaInl" alt="$ \beta $" src="form_83.png"/>, and base <img class="formulaInl" alt="$ \gamma $" src="form_84.png"/>.  <a href="classcaffe_1_1ExpLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Filler.html">Filler</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with constant or randomly-generated data.  <a href="classcaffe_1_1Filler.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1FilterLayer.html">FilterLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Takes two+ Blobs, interprets last <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> as a selector and filter remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, non-zero means that corresponding item needs to stay).  <a href="classcaffe_1_1FilterLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1FlattenLayer.html">FlattenLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Reshapes the input <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> into flat vectors.  <a href="classcaffe_1_1FlattenLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1GaussianFiller.html">GaussianFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with Gaussian-distributed values <img class="formulaInl" alt="$ x = a $" src="form_2.png"/>.  <a href="classcaffe_1_1GaussianFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1HDF5DataLayer.html">HDF5DataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> from HDF5 files.  <a href="classcaffe_1_1HDF5DataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1HDF5OutputLayer.html">HDF5OutputLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Write blobs to disk as HDF5 files.  <a href="classcaffe_1_1HDF5OutputLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1HingeLossLayer.html">HingeLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the hinge loss for a one-of-many classification task.  <a href="classcaffe_1_1HingeLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Im2colLayer.html">Im2colLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">A helper for image operations that rearranges image regions into column vectors. Used by <a class="el" href="classcaffe_1_1ConvolutionLayer.html" title="Convolves the input image with a bank of learned filters, and (optionally) adds biases. ">ConvolutionLayer</a> to perform convolution by matrix multiplication.  <a href="classcaffe_1_1Im2colLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ImageDataLayer.html">ImageDataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> from image files.  <a href="classcaffe_1_1ImageDataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1InfogainLossLayer.html">InfogainLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">A generalization of <a class="el" href="classcaffe_1_1MultinomialLogisticLossLayer.html" title="Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predi...">MultinomialLogisticLossLayer</a> that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.  <a href="classcaffe_1_1InfogainLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1InnerProductLayer.html">InnerProductLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases.  <a href="classcaffe_1_1InnerProductLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1InputLayer.html">InputLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> by assigning tops directly.  <a href="classcaffe_1_1InputLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1InternalThread.html">InternalThread</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Layer.html">Layer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">An interface for the units of computation which can be composed into a <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a>.  <a href="classcaffe_1_1Layer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LayerRegisterer.html">LayerRegisterer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LayerRegistry.html">LayerRegistry</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LogLayer.html">LogLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes <img class="formulaInl" alt="$ y = log_{\gamma}(\alpha x + \beta) $" src="form_113.png"/>, as specified by the scale <img class="formulaInl" alt="$ \alpha $" src="form_72.png"/>, shift <img class="formulaInl" alt="$ \beta $" src="form_83.png"/>, and base <img class="formulaInl" alt="$ \gamma $" src="form_84.png"/>.  <a href="classcaffe_1_1LogLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LossLayer.html">LossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">An interface for <a class="el" href="classcaffe_1_1Layer.html" title="An interface for the units of computation which can be composed into a Net. ">Layer</a>s that take two <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>s as input &ndash; usually (1) predictions and (2) ground-truth labels &ndash; and output a singleton <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> representing the loss.  <a href="classcaffe_1_1LossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LRNLayer.html">LRNLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Normalize the input in a local region across or within feature maps.  <a href="classcaffe_1_1LRNLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LSTMLayer.html">LSTMLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Processes sequential inputs using a "Long Short-Term Memory" (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time.  <a href="classcaffe_1_1LSTMLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1LSTMUnitLayer.html">LSTMUnitLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">A helper for <a class="el" href="classcaffe_1_1LSTMLayer.html" title="Processes sequential inputs using a &quot;Long Short-Term Memory&quot; (LSTM) [1] style recurrent neural networ...">LSTMLayer</a>: computes a single timestep of the non-linearity of the LSTM, producing the updated cell and hidden states.  <a href="classcaffe_1_1LSTMUnitLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1MemoryDataLayer.html">MemoryDataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> from memory.  <a href="classcaffe_1_1MemoryDataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1MSRAFiller.html">MSRAFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with values <img class="formulaInl" alt="$ x \sim N(0, \sigma^2) $" src="form_7.png"/> where <img class="formulaInl" alt="$ \sigma^2 $" src="form_8.png"/> is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.  <a href="classcaffe_1_1MSRAFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1MultinomialLogisticLossLayer.html">MultinomialLogisticLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input.  <a href="classcaffe_1_1MultinomialLogisticLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1MVNLayer.html">MVNLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Normalizes the input to have 0-mean and/or unit (1) variance.  <a href="classcaffe_1_1MVNLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1NesterovSolver.html">NesterovSolver</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Net.html">Net</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Connects <a class="el" href="classcaffe_1_1Layer.html" title="An interface for the units of computation which can be composed into a Net. ">Layer</a>s together into a directed acyclic graph (DAG) specified by a NetParameter.  <a href="classcaffe_1_1Net.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1NeuronLayer.html">NeuronLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">An interface for layers that take one blob as input ( <img class="formulaInl" alt="$ x $" src="form_11.png"/>) and produce one equally-sized blob as output ( <img class="formulaInl" alt="$ y $" src="form_13.png"/>), where each element of the output depends only on the corresponding input element.  <a href="classcaffe_1_1NeuronLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ParameterLayer.html">ParameterLayer</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1PoolingLayer.html">PoolingLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Pools the input image by taking the max, average, etc. within regions.  <a href="classcaffe_1_1PoolingLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1PositiveUnitballFiller.html">PositiveUnitballFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with values <img class="formulaInl" alt="$ x \in [0, 1] $" src="form_3.png"/> such that <img class="formulaInl" alt="$ \forall i \sum_j x_{ij} = 1 $" src="form_4.png"/>.  <a href="classcaffe_1_1PositiveUnitballFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1PowerLayer.html">PowerLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes <img class="formulaInl" alt="$ y = (\alpha x + \beta) ^ \gamma $" src="form_127.png"/>, as specified by the scale <img class="formulaInl" alt="$ \alpha $" src="form_72.png"/>, shift <img class="formulaInl" alt="$ \beta $" src="form_83.png"/>, and power <img class="formulaInl" alt="$ \gamma $" src="form_84.png"/>.  <a href="classcaffe_1_1PowerLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1PReLULayer.html">PReLULayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Parameterized Rectified Linear Unit non-linearity <img class="formulaInl" alt="$ y_i = \max(0, x_i) + a_i \min(0, x_i) $" src="form_130.png"/>. The differences from <a class="el" href="classcaffe_1_1ReLULayer.html" title="Rectified Linear Unit non-linearity . The simple max is fast to compute, and the function does not sa...">ReLULayer</a> are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels.  <a href="classcaffe_1_1PReLULayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1PythonLayer.html">PythonLayer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1RecurrentLayer.html">RecurrentLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">An abstract class for implementing recurrent behavior inside of an unrolled network. This <a class="el" href="classcaffe_1_1Layer.html" title="An interface for the units of computation which can be composed into a Net. ">Layer</a> type cannot be instantiated &ndash; instead, you should use one of its implementations which defines the recurrent architecture, such as <a class="el" href="classcaffe_1_1RNNLayer.html" title="Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network...">RNNLayer</a> or <a class="el" href="classcaffe_1_1LSTMLayer.html" title="Processes sequential inputs using a &quot;Long Short-Term Memory&quot; (LSTM) [1] style recurrent neural networ...">LSTMLayer</a>.  <a href="classcaffe_1_1RecurrentLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ReductionLayer.html">ReductionLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute "reductions" &ndash; operations that return a scalar output <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> for an input <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> of arbitrary size, such as the sum, absolute sum, and sum of squares.  <a href="classcaffe_1_1ReductionLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ReLULayer.html">ReLULayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Rectified Linear Unit non-linearity <img class="formulaInl" alt="$ y = \max(0, x) $" src="form_151.png"/>. The simple max is fast to compute, and the function does not saturate.  <a href="classcaffe_1_1ReLULayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ReshapeLayer.html">ReshapeLayer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1RMSPropSolver.html">RMSPropSolver</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1RNNLayer.html">RNNLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time.  <a href="classcaffe_1_1RNNLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ScaleLayer.html">ScaleLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the elementwise product of two input Blobs, with the shape of the latter <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> "broadcast" to match the shape of the former. Equivalent to tiling the latter <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>, then computing the elementwise product. Note: for efficiency and convenience, this layer can additionally perform a "broadcast" sum too when <code>bias_term: true</code> is set.  <a href="classcaffe_1_1ScaleLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SGDSolver.html">SGDSolver</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Optimizes the parameters of a <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> using stochastic gradient descent (SGD) with momentum.  <a href="classcaffe_1_1SGDSolver.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SigmoidCrossEntropyLossLayer.html">SigmoidCrossEntropyLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the cross-entropy (logistic) loss <img class="formulaInl" alt="$ E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) \right] $" src="form_163.png"/>, often used for predicting targets interpreted as probabilities.  <a href="classcaffe_1_1SigmoidCrossEntropyLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SigmoidLayer.html">SigmoidLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sigmoid function non-linearity <img class="formulaInl" alt="$ y = (1 + \exp(-x))^{-1} $" src="form_169.png"/>, a classic choice in neural networks.  <a href="classcaffe_1_1SigmoidLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SignalHandler.html">SignalHandler</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SilenceLayer.html">SilenceLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Ignores bottom blobs while producing no top blobs. (This is useful to suppress outputs during testing.)  <a href="classcaffe_1_1SilenceLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SliceLayer.html">SliceLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Takes a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> and slices it along either the num or channel dimension, outputting multiple sliced <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> results.  <a href="classcaffe_1_1SliceLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SoftmaxLayer.html">SoftmaxLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the softmax function.  <a href="classcaffe_1_1SoftmaxLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SoftmaxWithLossLayer.html">SoftmaxWithLossLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the multinomial logistic loss for a one-of-many classification task, passing real-valued predictions through a softmax to get a probability distribution over classes.  <a href="classcaffe_1_1SoftmaxWithLossLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Solver.html">Solver</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">An interface for classes that perform optimization on <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a>s.  <a href="classcaffe_1_1Solver.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SolverRegisterer.html">SolverRegisterer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SolverRegistry.html">SolverRegistry</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SplitLayer.html">SplitLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Creates a "split" path in the network by copying the bottom <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> into multiple top <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a>s to be used by multiple consuming layers.  <a href="classcaffe_1_1SplitLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SPPLayer.html">SPPLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size.  <a href="classcaffe_1_1SPPLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1SyncedMemory.html">SyncedMemory</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manages memory allocation and synchronization between the host (CPU) and device (GPU).  <a href="classcaffe_1_1SyncedMemory.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1TanHLayer.html">TanHLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">TanH hyperbolic tangent non-linearity <img class="formulaInl" alt="$ y = \frac{\exp(2x) - 1}{\exp(2x) + 1} $" src="form_172.png"/>, popular in auto-encoders.  <a href="classcaffe_1_1TanHLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1ThresholdLayer.html">ThresholdLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise.  <a href="classcaffe_1_1ThresholdLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1TileLayer.html">TileLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Copy a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> along specified dimensions.  <a href="classcaffe_1_1TileLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1Timer.html">Timer</a></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1UniformFiller.html">UniformFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with uniformly distributed values <img class="formulaInl" alt="$ x\sim U(a, b) $" src="form_1.png"/>.  <a href="classcaffe_1_1UniformFiller.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1WindowDataLayer.html">WindowDataLayer</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides data to the <a class="el" href="classcaffe_1_1Net.html" title="Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. ">Net</a> from windows of images files, specified by a window data file. This layer is <em>DEPRECATED</em> and only kept for archival purposes for use by the original R-CNN.  <a href="classcaffe_1_1WindowDataLayer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classcaffe_1_1XavierFiller.html">XavierFiller</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills a <a class="el" href="classcaffe_1_1Blob.html" title="A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...">Blob</a> with values <img class="formulaInl" alt="$ x \sim U(-a, +a) $" src="form_5.png"/> where <img class="formulaInl" alt="$ a $" src="form_6.png"/> is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.  <a href="classcaffe_1_1XavierFiller.html#details">More...</a><br /></td></tr>
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Typedefs</h2></td></tr>
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typedef boost::function&lt; SolverAction::Enum()&gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacecaffe.html#a79ce9ffbdd44b367252e0b8cf99bf430">ActionCallback</a></td></tr>
<tr class="memdesc:a79ce9ffbdd44b367252e0b8cf99bf430"><td class="mdescLeft">&#160;</td><td class="mdescRight">Type of a function that returns a <a class="el" href="classcaffe_1_1Solver.html" title="An interface for classes that perform optimization on Nets. ">Solver</a> Action enumeration. <br /></td></tr>
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typedef boost::mt19937&#160;</td><td class="memItemRight" valign="bottom"><b>rng_t</b></td></tr>
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Functions</h2></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>GlobalInit</b> (int *pargc, char ***pargv)</td></tr>
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<tr class="memitem:a819e1b2fe30b54d6df0fd88e646ec126"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="classcaffe_1_1Filler.html">Filler</a>&lt; Dtype &gt; *&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacecaffe.html#a819e1b2fe30b54d6df0fd88e646ec126">GetFiller</a> (const FillerParameter &amp;param)</td></tr>
<tr class="memdesc:a819e1b2fe30b54d6df0fd88e646ec126"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a specific filler from the specification given in FillerParameter.  <a href="#a819e1b2fe30b54d6df0fd88e646ec126">More...</a><br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>CaffeMallocHost</b> (void **ptr, size_t size, bool *use_cuda)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>CaffeFreeHost</b> (void *ptr, bool use_cuda)</td></tr>
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const char *&#160;</td><td class="memItemRight" valign="bottom"><b>cublasGetErrorString</b> (cublasStatus_t error)</td></tr>
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const char *&#160;</td><td class="memItemRight" valign="bottom"><b>curandGetErrorString</b> (curandStatus_t error)</td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><b>CAFFE_GET_BLOCKS</b> (const int N)</td></tr>
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std::string&#160;</td><td class="memItemRight" valign="bottom"><b>format_int</b> (int n, int numberOfLeadingZeros=0)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:ab9688e52d4b07eb170658282ab0d5e1b"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_load_nd_dataset_helper</b> (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; Dtype &gt; *blob, bool reshape)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:acc6c659efb146464737ad573f6662e0b"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_load_nd_dataset</b> (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; Dtype &gt; *blob, bool reshape=false)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a54a9b96a2fd60bdd4df802248d1d241a"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_save_nd_dataset</b> (const hid_t file_id, const string &amp;dataset_name, const <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; Dtype &gt; &amp;blob, bool write_diff=false)</td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_load_int</b> (hid_t loc_id, const string &amp;dataset_name)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_save_int</b> (hid_t loc_id, const string &amp;dataset_name, int i)</td></tr>
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string&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_load_string</b> (hid_t loc_id, const string &amp;dataset_name)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_save_string</b> (hid_t loc_id, const string &amp;dataset_name, const string &amp;s)</td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_get_num_links</b> (hid_t loc_id)</td></tr>
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string&#160;</td><td class="memItemRight" valign="bottom"><b>hdf5_get_name_by_idx</b> (hid_t loc_id, int idx)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
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template&lt;typename Dtype &gt; </td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>InsertSplits</b> (const NetParameter &amp;param, NetParameter *param_split)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>ConfigureSplitLayer</b> (const string &amp;layer_name, const string &amp;blob_name, const int blob_idx, const int split_count, const float loss_weight, LayerParameter *split_layer_param)</td></tr>
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string&#160;</td><td class="memItemRight" valign="bottom"><b>SplitLayerName</b> (const string &amp;layer_name, const string &amp;blob_name, const int blob_idx)</td></tr>
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string&#160;</td><td class="memItemRight" valign="bottom"><b>SplitBlobName</b> (const string &amp;layer_name, const string &amp;blob_name, const int blob_idx, const int split_idx)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>MakeTempDir</b> (string *temp_dirname)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>ReadProtoFromTextFileOrDie</b> (const string &amp;filename, Message *proto)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>WriteProtoToTextFile</b> (const Message &amp;proto, const char *filename)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>WriteProtoToTextFile</b> (const Message &amp;proto, const string &amp;filename)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadProtoFromBinaryFile</b> (const char *filename, Message *proto)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadProtoFromBinaryFile</b> (const string &amp;filename, Message *proto)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>ReadProtoFromBinaryFileOrDie</b> (const char *filename, Message *proto)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>ReadProtoFromBinaryFileOrDie</b> (const string &amp;filename, Message *proto)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>WriteProtoToBinaryFile</b> (const Message &amp;proto, const char *filename)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>WriteProtoToBinaryFile</b> (const Message &amp;proto, const string &amp;filename)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadFileToDatum</b> (const string &amp;filename, const int label, Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadFileToDatum</b> (const string &amp;filename, Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadImageToDatum</b> (const string &amp;filename, const int label, const int height, const int width, const bool is_color, const std::string &amp;encoding, Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadImageToDatum</b> (const string &amp;filename, const int label, const int height, const int width, const bool is_color, Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>ReadImageToDatum</b> (const string &amp;filename, const int label, const std::string &amp;encoding, Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>DecodeDatumNative</b> (Datum *datum)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>DecodeDatum</b> (Datum *datum, bool is_color)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
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template&lt;typename Dtype &gt; </td></tr>
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const char *&#160;</td><td class="memItemRight" valign="bottom"><b>UpgradeV1LayerType</b> (const V1LayerParameter_LayerType type)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>NetNeedsInputUpgrade</b> (const NetParameter &amp;net_param)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>UpgradeNetInput</b> (NetParameter *net_param)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>NetNeedsBatchNormUpgrade</b> (const NetParameter &amp;net_param)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>SolverNeedsTypeUpgrade</b> (const SolverParameter &amp;solver_param)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>UpgradeSolverType</b> (SolverParameter *solver_param)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>UpgradeSolverAsNeeded</b> (const string &amp;param_file, SolverParameter *param)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>ReadSolverParamsFromTextFileOrDie</b> (const string &amp;param_file, SolverParameter *param)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1TileLayer.html">TileLayer</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_LAYER_CLASS</b> (Tile)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1Net.html">Net</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1Solver.html">Solver</a>)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a6371e76ca471695989f83769105dbce0"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>adadelta_update_gpu</b> (int N, Dtype *g, Dtype *h, Dtype *h2, Dtype momentum, Dtype delta, Dtype local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1AdaDeltaSolver.html">AdaDeltaSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (AdaDelta)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a5f5a95a53781fee3860a7b2649775c77"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>adagrad_update_gpu</b> (int N, Dtype *g, Dtype *h, Dtype delta, Dtype local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1AdaGradSolver.html">AdaGradSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (AdaGrad)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a483e041e0c80d67e254f52003bf2ef27"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>adam_update_gpu</b> (int N, Dtype *g, Dtype *m, Dtype *v, Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1AdamSolver.html">AdamSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (Adam)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a98a8eff88f111dba635bdc3f6e0498b1"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>nesterov_update_gpu</b> (int N, Dtype *g, Dtype *h, Dtype momentum, Dtype local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1NesterovSolver.html">NesterovSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (Nesterov)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a39053f2a38c1044610a95c4d43588540"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>rmsprop_update_gpu</b> (int N, Dtype *g, Dtype *h, Dtype rms_decay, Dtype delta, Dtype local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1RMSPropSolver.html">RMSPropSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (RMSProp)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a7b52a25aea6e34816f4758e6d12f7feb"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>sgd_update_gpu</b> (int N, Dtype *g, Dtype *h, Dtype momentum, Dtype local_rate)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>INSTANTIATE_CLASS</b> (<a class="el" href="classcaffe_1_1SGDSolver.html">SGDSolver</a>)</td></tr>
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&#160;</td><td class="memItemRight" valign="bottom"><b>REGISTER_SOLVER_CLASS</b> (SGD)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a82f7e8e73b156548fa7b90e67024ca1d"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_load_nd_dataset&lt; float &gt;</b> (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; float &gt; *blob, bool reshape)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a33e1d4fe0ec40d1186387c7872f4c99d"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_load_nd_dataset&lt; double &gt;</b> (hid_t file_id, const char *dataset_name_, int min_dim, int max_dim, <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; double &gt; *blob, bool reshape)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:aea2d44137826e942cabd48bf54abbc21"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_save_nd_dataset&lt; float &gt;</b> (const hid_t file_id, const string &amp;dataset_name, const <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; float &gt; &amp;blob, bool write_diff)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a91c74b78f13fb2d6fba4f45ead5480bf"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>hdf5_save_nd_dataset&lt; double &gt;</b> (hid_t file_id, const string &amp;dataset_name, const <a class="el" href="classcaffe_1_1Blob.html">Blob</a>&lt; double &gt; &amp;blob, bool write_diff)</td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>is_a_ge_zero_and_a_lt_b</b> (int a, int b)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>im2col_cpu&lt; float &gt;</b> (const float *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, float *data_col)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>im2col_cpu&lt; double &gt;</b> (const double *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, double *data_col)</td></tr>
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template&lt;typename Dtype &gt; </td></tr>
<tr class="memitem:a85bd5b073b9dce6e47dcc087f1241a14"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>im2col_nd_core_cpu</b> (const Dtype *data_input, const bool im2col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, Dtype *data_output)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>im2col_nd_cpu&lt; float &gt;</b> (const float *data_im, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, float *data_col)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>im2col_nd_cpu&lt; double &gt;</b> (const double *data_im, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, double *data_col)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>col2im_cpu&lt; float &gt;</b> (const float *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, float *data_im)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>col2im_cpu&lt; double &gt;</b> (const double *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, double *data_im)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>col2im_nd_cpu&lt; float &gt;</b> (const float *data_col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, float *data_im)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>col2im_nd_cpu&lt; double &gt;</b> (const double *data_col, const int num_spatial_axes, const int *im_shape, const int *col_shape, const int *kernel_shape, const int *pad, const int *stride, const int *dilation, double *data_im)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:af2b584d464ec95b349ba159204d58ba7"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_gemm&lt; float &gt;</b> (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const float alpha, const float *A, const float *B, const float beta, float *C)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:aa83e160e90266206ce3baf458a21d884"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_gemm&lt; double &gt;</b> (const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const double alpha, const double *A, const double *B, const double beta, double *C)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:ab5a4363a871d7f6efa2d0fb201ca986a"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_gemv&lt; float &gt;</b> (const CBLAS_TRANSPOSE TransA, const int M, const int N, const float alpha, const float *A, const float *x, const float beta, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:afd43f6e27055e68628f6eb42f8416407"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_gemv&lt; double &gt;</b> (const CBLAS_TRANSPOSE TransA, const int M, const int N, const double alpha, const double *A, const double *x, const double beta, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:aaa105c828f9c1423a07a6edbf9dd44cd"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_axpy&lt; float &gt;</b> (const int N, const float alpha, const float *X, float *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a1429069b15b332c453b637fda744bc81"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_axpy&lt; double &gt;</b> (const int N, const double alpha, const double *X, double *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_set&lt; int &gt;</b> (const int N, const int alpha, int *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_set&lt; float &gt;</b> (const int N, const float alpha, float *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_set&lt; double &gt;</b> (const int N, const double alpha, double *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a90978c3c773431b11c882e2a036bd2c2"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_add_scalar</b> (const int N, const float alpha, float *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a8894a527bd87ff85fd7f480ed4876917"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_add_scalar</b> (const int N, const double alpha, double *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_copy&lt; int &gt;</b> (const int N, const int *X, int *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_copy&lt; unsigned int &gt;</b> (const int N, const unsigned int *X, unsigned int *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_copy&lt; float &gt;</b> (const int N, const float *X, float *Y)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_copy&lt; double &gt;</b> (const int N, const double *X, double *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a53ccb0c1449e14351b86a919948d1a87"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_scal&lt; float &gt;</b> (const int N, const float alpha, float *X)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a11cf505d7d4980d3d8b2eb84f76bc969"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_scal&lt; double &gt;</b> (const int N, const double alpha, double *X)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a80cee6f8f71d4d64423b07251a369c98"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_axpby&lt; float &gt;</b> (const int N, const float alpha, const float *X, const float beta, float *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:acdfc74a60bc50fffe61fd27484b6b795"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_axpby&lt; double &gt;</b> (const int N, const double alpha, const double *X, const double beta, double *Y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a33de0e8c285c49d3388dd9b36cfae096"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_add&lt; float &gt;</b> (const int n, const float *a, const float *b, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a7e191890a0b953fafbb5cabb94c185fd"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_add&lt; double &gt;</b> (const int n, const double *a, const double *b, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:ad95d5c303539634141319733138a1a30"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sub&lt; float &gt;</b> (const int n, const float *a, const float *b, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:afbe160a31cb8ba24b9a458b29dd30b29"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sub&lt; double &gt;</b> (const int n, const double *a, const double *b, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a6c2ce80409bbcc2f7628245d0d0961d8"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_mul&lt; float &gt;</b> (const int n, const float *a, const float *b, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:aa737440327ff64cf24422620fcb7b5f2"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_mul&lt; double &gt;</b> (const int n, const double *a, const double *b, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a5da01a3589fb3ed828d3c3ea28dca727"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_div&lt; float &gt;</b> (const int n, const float *a, const float *b, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a6aff3d4f403573c6116ea52e0875667c"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_div&lt; double &gt;</b> (const int n, const double *a, const double *b, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:ac63faf91a74ed26c21995906ffc05aa4"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_powx&lt; float &gt;</b> (const int n, const float *a, const float b, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:abc32d9c6721f0a864270552957c513af"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_powx&lt; double &gt;</b> (const int n, const double *a, const double b, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:add3fbffad218dec24176292a0fb2436a"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sqr&lt; float &gt;</b> (const int n, const float *a, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a5b56a825d8809e999548bbfe94b3bb5b"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sqr&lt; double &gt;</b> (const int n, const double *a, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a972c4f9b0e5adbde79244f7d233b7416"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sqrt&lt; float &gt;</b> (const int n, const float *a, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a83d092226d5f4af982a5ee4670a8f81d"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_sqrt&lt; double &gt;</b> (const int n, const double *a, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a0553e22b5a2615ce54bb22bab6a4df15"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_exp&lt; float &gt;</b> (const int n, const float *a, float *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:ab934b2004cda176643660f2029ff9fa2"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_exp&lt; double &gt;</b> (const int n, const double *a, double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:acb0328e4604ee2bb2829c1ab069a047b"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_log&lt; float &gt;</b> (const int n, const float *a, float *y)</td></tr>
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template&lt;&gt; </td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:ae723398dd9f752078438ba1e9e07f084"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_abs&lt; float &gt;</b> (const int n, const float *a, float *y)</td></tr>
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template&lt;&gt; </td></tr>
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template float&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_nextafter</b> (const float b)</td></tr>
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template double&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_nextafter</b> (const double b)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_uniform&lt; float &gt;</b> (const int n, const float a, const float b, float *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_uniform&lt; double &gt;</b> (const int n, const double a, const double b, double *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_gaussian&lt; float &gt;</b> (const int n, const float mu, const float sigma, float *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_gaussian&lt; double &gt;</b> (const int n, const double mu, const double sigma, double *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_bernoulli&lt; double &gt;</b> (const int n, const double p, int *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_bernoulli&lt; float &gt;</b> (const int n, const float p, int *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_bernoulli&lt; double &gt;</b> (const int n, const double p, unsigned int *r)</td></tr>
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template void&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_rng_bernoulli&lt; float &gt;</b> (const int n, const float p, unsigned int *r)</td></tr>
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template&lt;&gt; </td></tr>
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template float&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_cpu_dot&lt; float &gt;</b> (const int n, const float *x, const float *y)</td></tr>
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template double&#160;</td><td class="memItemRight" valign="bottom"><b>caffe_cpu_dot&lt; double &gt;</b> (const int n, const double *x, const double *y)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a1e9591a34f95ee26700fc5e60d35518c"><td class="memTemplItemLeft" align="right" valign="top">double&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_asum&lt; double &gt;</b> (const int n, const double *x)</td></tr>
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template&lt;&gt; </td></tr>
<tr class="memitem:a8652e05695145f9dfd4304117106a8f6"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><b>caffe_cpu_scale&lt; float &gt;</b> (const int n, const float alpha, const float *x, float *y)</td></tr>
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template&lt;&gt; </td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="var-members"></a>
Variables</h2></td></tr>
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const float&#160;</td><td class="memItemRight" valign="bottom"><b>kLOG_THRESHOLD</b> = 1e-20</td></tr>
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const int&#160;</td><td class="memItemRight" valign="bottom"><b>CAFFE_CUDA_NUM_THREADS</b> = 512</td></tr>
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const Dtype&#160;</td><td class="memItemRight" valign="bottom"><b>alpha</b></td></tr>
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const Dtype const Dtype *&#160;</td><td class="memItemRight" valign="bottom"><b>x</b></td></tr>
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const Dtype const Dtype Dtype *&#160;</td><td class="memItemRight" valign="bottom"><b>y</b></td></tr>
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const float&#160;</td><td class="memItemRight" valign="bottom"><b>kBNLL_THRESHOLD</b> = 50.</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>A layer factory that allows one to register layers. During runtime, registered layers can be called by passing a LayerParameter protobuffer to the CreateLayer function: </p>
<p>A solver factory that allows one to register solvers, similar to layer factory. During runtime, registered solvers could be called by passing a SolverParameter protobuffer to the CreateSolver function:</p>
<p>LayerRegistry&lt;Dtype&gt;::CreateLayer(param);</p>
<p>There are two ways to register a layer. Assuming that we have a layer like:</p>
<p>template &lt;typename dtype&gt;=""&gt; class MyAwesomeLayer : public Layer&lt;Dtype&gt; { // your implementations };</p>
<p>and its type is its C++ class name, but without the "Layer" at the end ("MyAwesomeLayer" -&gt; "MyAwesome").</p>
<p>If the layer is going to be created simply by its constructor, in your c++ file, add the following line:</p>
<p>REGISTER_LAYER_CLASS(MyAwesome);</p>
<p>Or, if the layer is going to be created by another creator function, in the format of:</p>
<p>template &lt;typename dtype&gt;=""&gt; Layer&lt;Dtype*&gt; GetMyAwesomeLayer(const LayerParameter&amp; param) { // your implementation }</p>
<p>(for example, when your layer has multiple backends, see GetConvolutionLayer for a use case), then you can register the creator function instead, like</p>
<p>REGISTER_LAYER_CREATOR(MyAwesome, GetMyAwesomeLayer)</p>
<p>Note that each layer type should only be registered once.</p>
<p>SolverRegistry&lt;Dtype&gt;::CreateSolver(param);</p>
<p>There are two ways to register a solver. Assuming that we have a solver like:</p>
<p>template &lt;typename dtype&gt;=""&gt; class MyAwesomeSolver : public Solver&lt;Dtype&gt; { // your implementations };</p>
<p>and its type is its C++ class name, but without the "Solver" at the end ("MyAwesomeSolver" -&gt; "MyAwesome").</p>
<p>If the solver is going to be created simply by its constructor, in your C++ file, add the following line:</p>
<p>REGISTER_SOLVER_CLASS(MyAwesome);</p>
<p>Or, if the solver is going to be created by another creator function, in the format of:</p>
<p>template &lt;typename dtype&gt;=""&gt; Solver&lt;Dtype*&gt; GetMyAwesomeSolver(const SolverParameter&amp; param) { // your implementation }</p>
<p>then you can register the creator function instead, like</p>
<p>REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver)</p>
<p>Note that each solver type should only be registered once. </p>
</div><h2 class="groupheader">Function Documentation</h2>
<a id="a819e1b2fe30b54d6df0fd88e646ec126"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a819e1b2fe30b54d6df0fd88e646ec126">&#9670;&nbsp;</a></span>GetFiller()</h2>

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template&lt;typename Dtype &gt; </div>
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          <td class="paramtype">const FillerParameter &amp;&#160;</td>
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<p>Get a specific filler from the specification given in FillerParameter. </p>
<p>Ideally this would be replaced by a factory pattern, but we will leave it this way for now. </p>

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